Here, we posit a simple theory of brain network control, and we test its biological validity and utility in combined electrocorticography (ECoG) and diffusion weighted imaging (DWI) data from patients with medically refractory epilepsy undergoing evaluation for resective surgery. For each subject, we constructed a structural brain network where nodes represented regions of the Lausanne atlas [1]} and where edges represented quantitative anisotropy between these regions estimated from diffusion tractography [2]} (Fig. REF A). Upon this network, we stipulated a noise-free, linear, continuous-time, and time-invariant model of network dynamics [3]}, [4]}, [5]}, [6]}, [7]}, from which we built predictions about how regional activity would deviate from its initial state in the presence of exogenous control input to any given node. Using ECoG data acquired from the same individuals during an extensive direct electrical stimulation regimen (Fig. REF B), we test these theoretical predictions by representing (i) regional activity as an electrode's power in a given frequency band, (ii) the pre-stimulation state as the power prior to stimulation, and (iii) the post-stimulation state as the power after stimulation (Fig. REF C). After quantifying the relative accuracy of our theoretical predictions, we next use the model to make more specific predictions about the control energy required to optimally guide the brain from a pre-stimulation state to a state associated with good memory encoding. We then test these predictions using subject-level power-based biomarkers of good memory encoding extracted with a multivariate classifier from ECoG data collected during a verbal memory task [8]}. Finally, we investigate how certain topological [7]} and spatial [10]} properties of a subject's network alter its response to direct electrical stimulation, and we ask whether that response is also modulated by control properties of the area being stimulated [3]}, [12]}. Essentially, our study posits and empirically tests a simple theory of brain network control, demonstrating its utility in predicting response to direct electrical stimulation.
